微博用户线下行为动机与线上使用行为的相关性研究

采用在线问卷调查和网络信息抽取技术相结合的方法,获取微博用户线下行为动机和线上使用行为数据,并依此确定了微博用户8种线上使用行为与4种线下行为动机。通过对样本数据相关性分析,发现女性微博使用动机要高于男性,并且微博使用动机与用户年龄具有显著的负相关性;此外研究还表明,不同的行为动机对应着相关的使用行为。上述研究有利于提升对网络主体的活动规律的了解。     

Guazia,the earliest ovule without cupule but with unique integumentary lobes

The earliest ovules in the Late Devonian (Famennian) are surrounded by a cupule that is involved in both protection and pollination, and generally have free integumentary lobes. Here we report a new taxon from the Famennian of China, Guazia dongzhiensis gen. et sp. nov. The terminally borne ovule is apparently acupulate (without cupule) and has four radially arranged wing-like integumentary lobes that are extensively fused, and folded lengthwise and inwards. Guazia provides evidence that not all Devonian seeds possess a cupule and it increases their diversity in integumentary lobes. This genus also suggests that the integuments develop new functions, probably including wind dispersal at the expense of the cupules.     

跨组织研发项目多主体合作有限理性下的治理机制

探讨跨组织项目相关多主体合作问题,基于有限理性视角,从管理运作、关系协调等协同治理机制来分析影响协同治理效果的因素,建立跨组织研发项目多主体合作协同治理框架.理论研究和数值仿真结果表明:跨组织研发项目相关多主体合作之前所具备的基础收益对项目生成期没有影响,外部诱惑变大、企业信誉下降会降低整个寿命周期项目合作联盟系统的稳定性,将合作收益分配比例控制在一定范围内才能达到促进多主体合作的效果;项目多主体需控制合作成本和协同成本,防止协同关系破裂.企业信誉贯穿跨组织合作整个项目周期,因此合作多主体维持信誉显得尤为重要.     

Proteus仿真软件在电子技术课程中的应用

本文阐述了在电子技术教学过程中,运用Proteus仿真软件对模拟电子技术及数字电子技术实验电路进行仿真设计与验证调试的实例。运用该软件能够激发学生的学习兴趣,明显改善教学效果。     

过度自信CEO项目价值预判能力、投资心理及薪酬契约研究

研究过度自信CEO项目价值预判能力对其投资心理的影响作用以及激励CEO主动承担项目的薪酬契约特点后发现:在预判结果与项目期望价值相同的条件下,当项目价值预判能力足够大时,CEO会认为进行投资能使公司获得收益;随着项目价值预判能力与过度自信水平的不断提高,激励CEO主动承担项目的高水平薪酬的最大值会不断提高,而其相对于中等水平薪酬的增量的最小值会不断降低.     

专利诉讼的网络分析及主体类型研究

近年来,专利诉讼呈现快速增长的态势,深刻影响着技术创新和产业竞争的格局。以智能手机产业为研究样本,运用社会网络分析方法,研究揭示诉讼主体间的复杂关系,比较分析诉讼主体的差异;在此基础上,采用层次聚类分析方法,定量分析诉讼主体的关键特征,并对诉讼主体进行类型划分。研究表明,诉讼主体在起诉频度、被诉频度和网络位置等方面存在显著差异,据此可以划分为四种类型,在领先企业和追赶企业以外,还存在两类被忽视的诉讼主体,主要为从事专利运营的专业机构;应对专利诉讼不但要重视传统的行业竞争者,而且要高度警惕以上非传统的诉讼主体。     

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.     

无线中继网络的联合路由与调度的近似最优算法

在基于802.16j的无线中继网络中,考虑路由和调度的联合优化问题,最小化系统总调度时间. 首先采用线性规划的方法建立路由,进行链路业务速率分配,然后基于平移和交换思想提出一种链路调度算法. 理论分析证明所提算法的性能在最坏情况下,不会超过最优性能的1.5倍. 仿真结果表明,所提算法的平均性能非常接近最优性能.     

可产生潜在威胁的网络数据挖掘模型仿真分析

对可产生潜在威胁的网络数据进行挖掘的过程中,采用传统关联规则方法无法运算潜在威胁数据的关联性,降低潜在威胁数据挖掘的准确性,提出一种基于区域频繁扩展树的支持向量机回归算法,用于挖掘网络潜在威胁数据,构建潜在威胁下网络数据的区域频繁扩展树,获取潜在威胁下网络数据间的关联性,依据潜威胁下网络数据的关联性塑造基于支持向量机回归网络潜在威胁数据挖掘模型,模型利用支持向量回归估计模型分析数据回归曲线的平稳度,对比回归预测值同真实值间残差,对检测数据中的潜在威胁数据进行识别,完成潜在威胁下网络数据的准确挖掘。实验结果说明,采用所提方法对不同复杂度潜在威胁下的网络数据进行挖掘,具有较高精确度。